17 research outputs found
Mittag--Leffler Euler integrator and large deviations for stochastic space-time fractional diffusion equations
Stochastic space-time fractional diffusion equations often appear in the
modeling of the heat propagation in non-homogeneous medium. In this paper, we
firstly investigate the Mittag--Leffler Euler integrator of a class of
stochastic space-time fractional diffusion equations, whose super-convergence
order is obtained by developing a helpful decomposition way for the
time-fractional integral. Here, the developed decomposition way is the key to
dealing with the singularity of the solution operator. Moreover, we study the
Freidlin--Wentzell type large deviation principles of the underlying equation
and its Mittag--Leffler Euler integrator based on the weak convergence
approach. In particular, we prove that the large deviation rate function of the
Mittag--Leffler Euler integrator -converges to that of the underlying
equation
Numerical approximations of one-point large deviations rate functions of stochastic differential equations with small noise
In this paper, we study the numerical approximation of the one-point large
deviations rate functions of nonlinear stochastic differential equations (SDEs)
with small noise. We show that the stochastic -method satisfies the
one-point large deviations principle with a discrete rate function for
sufficiently small step-size, and present a uniform error estimate between the
discrete rate function and the continuous one on bounded sets in terms of
step-size. It is proved that the convergence orders in the cases of
multiplicative noises and additive noises are and respectively. Based
on the above results, we obtain an effective approach to numerically
approximating the large deviations rate functions of nonlinear SDEs with small
time. To the best of our knowledge, this is the first result on the convergence
rate of discrete rate functions for approximating the one-point large
deviations rate functions associated with nonlinear SDEs with small noise
Error analysis of numerical methods on graded meshes for stochastic Volterra equations
This paper presents the error analysis of numerical methods on graded meshes
for stochastic Volterra equations with weakly singular kernels. We first prove
a novel regularity estimate for the exact solution via analyzing the associated
convolution structure. This reveals that the exact solution exhibits an initial
singularity in the sense that its H\"older continuous exponent on any
neighborhood of is lower than that on every compact subset of .
Motivated by the initial singularity, we then construct the Euler--Maruyama
method, fast Euler--Maruyama method, and Milstein method based on graded
meshes. By establishing their pointwise-in-time error estimates, we give the
grading exponents of meshes to attain the optimal uniform-in-time convergence
orders, where the convergence orders improve those of the uniform mesh case.
Numerical experiments are finally reported to confirm the sharpness of
theoretical findings
A splitting semi-implicit method for stochastic incompressible Euler equations on
The main difficulty in studying numerical method for stochastic evolution
equations (SEEs) lies in the treatment of the time discretization (J. Printems.
[ESAIM Math. Model. Numer. Anal. (2001)]). Although fruitful results on
numerical approximations for SEEs have been developed, as far as we know, none
of them include that of stochastic incompressible Euler equations. To bridge
this gap, this paper proposes and analyses a splitting semi-implicit method in
temporal direction for stochastic incompressible Euler equations on torus
driven by an additive noise. By a Galerkin approximation and the
fixed point technique, we establish the unique solvability of the proposed
method. Based on the regularity estimates of both exact and numerical
solutions, we measure the error in and show that the
pathwise convergence order is nearly and the convergence order in
probability is almost
Convergence analysis of one-point large deviations rate functions of numerical discretizations for stochastic wave equations with small noise
In this work, we present the convergence analysis of one-point large
deviations rate functions (LDRFs) of the spatial finite difference method (FDM)
for stochastic wave equations with small noise, which is essentially about the
asymptotical limit of minimization problems and not a trivial task for the
nonlinear cases. In order to overcome the difficulty that objective functions
for the original equation and the spatial FDM have different effective domains,
we propose a new technical route for analyzing the pointwise convergence of the
one-point LDRFs of the spatial FDM, based on the -convergence of
objective functions. Based on the new technical route, the intractable
convergence analysis of one-point LDRFs boils down to the qualitative analysis
of skeleton equations of the original equation and its numerical
discretizations
Convergence of Density Approximations for Stochastic Heat Equation
This paper investigates the convergence of density approximations for
stochastic heat equation in both uniform convergence topology and total
variation distance. The convergence order of the densities in uniform
convergence topology is shown to be exactly in the nonlinear case and
nearly in the linear case. This result implies that the distributions of
the approximations always converge to the distribution of the origin equation
in total variation distance. As far as we know, this is the first result on the
convergence of density approximations to the stochastic partial differential
equation
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection
Machine learning based solutions have been successfully employed for
automatic detection of malware in Android applications. However, machine
learning models are known to lack robustness against inputs crafted by an
adversary. So far, the adversarial examples can only deceive Android malware
detectors that rely on syntactic features, and the perturbations can only be
implemented by simply modifying Android manifest. While recent Android malware
detectors rely more on semantic features from Dalvik bytecode rather than
manifest, existing attacking/defending methods are no longer effective. In this
paper, we introduce a new highly-effective attack that generates adversarial
examples of Android malware and evades being detected by the current models. To
this end, we propose a method of applying optimal perturbations onto Android
APK using a substitute model. Based on the transferability concept, the
perturbations that successfully deceive the substitute model are likely to
deceive the original models as well. We develop an automated tool to generate
the adversarial examples without human intervention to apply the attacks. In
contrast to existing works, the adversarial examples crafted by our method can
also deceive recent machine learning based detectors that rely on semantic
features such as control-flow-graph. The perturbations can also be implemented
directly onto APK's Dalvik bytecode rather than Android manifest to evade from
recent detectors. We evaluated the proposed manipulation methods for
adversarial examples by using the same datasets that Drebin and MaMadroid (5879
malware samples) used. Our results show that, the malware detection rates
decreased from 96% to 1% in MaMaDroid, and from 97% to 1% in Drebin, with just
a small distortion generated by our adversarial examples manipulation method.Comment: 15 pages, 11 figure